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Linear and Generalized Linear Mixed Models and Their Applications

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Éditeur :

Springer


Collection :

Springer Series in Statistics

Paru le : 2021-03-22

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Description

Now in its second edition, this book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
This book is suitable for students, researchers, and practitioners who are interested in using mixed models for statistical data analysis with public health applications. It is best for graduate courses in statistics, or for those who have taken a first course in mathematical statistics, are familiar with using computers for data analysis, and have a foundational background in calculus and linear algebra.
Pages
343 pages
Collection
Springer Series in Statistics
Parution
2021-03-22
Marque
Springer
EAN papier
9781071612811
EAN PDF
9781071612828
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
34
Taille du fichier
4301 Ko
Prix
126,59 €
EAN EPUB
9781071612828
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
34
Taille du fichier
15135 Ko
Prix
126,59 €

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